Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

This study aims to guide future research by examining trends and structures in scholarly publications about digital transformation in healthcare. We analyzed English-language, open-access journal articles related to this topic from the Scopus database, irrespective of publication year. Using tools like Microsoft Excel, VOSviewer, and Scopus Analyzer, we found a growing research interest in this area. The most influential article, despite being recent, has been cited 836 times, indicating its impact. Notably, both Western and Eastern countries contribute significantly to this field, with research spanning multiple disciplines, including computer science, medicine, engineering, business, social sciences, and health professions. Our findings can help policymakers allocate resources to impactful research areas, prioritize multidisciplinary collaboration, and promote international partnerships. They also offer insights for technology investment, implementation, and policy decisions. However, this study has limitations. It relied solely on Scopus data and didn’t consider factors like author affiliations. Future research should explore specific collaboration types and the ethical, social, policy, and governance implications of digital transformation in healthcare.

This article is devoted to studying the principles of the relationship between democracy and demoethics as tools for transforming the sustainable development of society. The study is based on the assumption that the effective functioning of democracy is associated with such social phenomena as elections and electoral behavior. The study examined electoral behavior and surveyed members of society about the qualities of candidates to which they pay special attention. An analysis of qualitative and quantitative data demonstrating the democratic foundations of elections of members of society was conducted, and an analysis of the choice of voters in the extraordinary elections of the President of the Republic of Kazakhstan by region was conducted. In this study, Bayesian network modeling is experimentally applied to formalize the problem of identifying and analyzing the behavior of virtuous personality traits. A sociological survey of public opinion was conducted using the questionnaire method with the participation of 826 people from all regions of Kazakhstan from May to June 2023. A questionnaire was used to collect data, the main purpose of which was to compare attitudes and find out what values are considered important for people, what norms of behavior are considered acceptable, and to understand what values and norms prevail in society. It is concluded that the concept of demoethics promotes a positive transformation of humanity and helps to form a new leader of virtue, a ruler of the city, capable of making ethical rational decisions that can ensure a balance between the economic, social, and environmental needs of humanity.

This study investigates the impact of artificial intelligence (AI) integration on preventing employee burnout through a human-centered, multimodal approach. Given the increasing prevalence of AI in workplace settings, this research seeks to understand how various dimensions of AI integration—such as the intensity of integration, employee training, personalization of AI tools, and the frequency of AI feedback—affect employee burnout. A quantitative approach was employed, involving a survey of 320 participants from high-stress sectors such as healthcare and IT. The findings reveal that the benefits of AI in reducing burnout are substantial yet highly dependent on the implementation strategy. Effective AI integration that includes comprehensive training, high personalization, and regular, constructive feedback correlates with lower levels of burnout. These results suggest that the mere introduction of AI technologies is insufficient for reducing burnout; instead, a holistic strategy that includes thorough employee training, tailored personalization, and continuous feedback is crucial for leveraging AI’s potential to alleviate workplace stress. This study provides valuable insights for organizational leaders and policymakers aiming to develop informed AI deployment strategies that prioritize employee well-being.

Realistic project scheduling and control are critical for running a profitable enterprise in the construction industry. Finance-based scheduling aims to produce more realistic schedules by considering both resource and cash constraints. Since the introduction of finance-based scheduling, its literature has evolved from a single-objective model to a multi-objective model and also from a single-project problem to a multi-project problem for a contractor. This study investigates the possibility of cooperation among contractors with concurrent projects to minimize financial costs. Contractors often do not use their entire credit and may be required to pay a penalty for the unused portions. Therefore, contractors are willing to share these unused portions to decrease their financing costs and consequently improve their overall profits. This study focuses on the partnering of two contractors in a joint finance-based scheduling where contractors are allowed to lend credit to or borrow credit from each other at an internal interest rate. We apply this approach to an illustrative example in which two concurrent projects have the potential for partnering. Results show that joint finance-based scheduling reduces the financing cost for both contractors and leads to additional overall profits. Our further analyses highlight the intricate dynamics impacting additional net profit, revealing optimal scenarios for cooperation in complex project networks.